Professional Overview
I have hands-on experience in Artificial Intelligence and Machine Learning, particularly in Generative AI, LLMs, RAG, multi-agent systems, NLP and deep learning. I have worked on building AI pipelines, integrating LLMs, developing intelligent agents, implementing document processing and information extraction, and designing retrieval systems using vector databases. I have experience with hybrid search techniques, embeddings, prompt engineering, knowledge graphs and GraphRAG. I have also worked with deep learning architectures such as CNNs and Graph Transformers, along with traditional machine learning algorithms for classification and prediction. Tools and technologies I use: Python, LangChain, LangGraph, Hugging Face, PyTorch, TensorFlow, Scikit-learn, Neo4j, Qdrant, Vector Databases, FastAPI, Azure AI Foundry, SQL, NumPy and Pandas.
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
